Push & Pull: autonomous deployment of mobile sensors for a complete coverage

Abstract

Mobile sensor networks are important for several strategic applications devoted to monitoring critical areas. In such hostile scenarios, sensors cannot be deployed manually and are either sent from a safe location or dropped from an aircraft. Mobile devices permit a dynamic deployment reconfiguration that improves the coverage in terms of completeness and uniformity. In this paper we propose a distributed algorithm for the autonomous deployment of mobile sensors called Push & Pull. According to our proposal, movement decisions are made by each sensor on the basis of locally available information and do not require any prior knowledge of the operating conditions or any manual tuning of key parameters. We formally prove that, when a sufficient number of sensors are available, our approach guarantees a complete and uniform coverage. Furthermore, we demonstrate that the algorithm execution always terminates preventing movement oscillations. Numerous simulations show that our algorithm reaches a complete coverage within reasonable time with moderate energy consumption, even when the target area has irregular shapes. Performance comparisons between Push & Pull and one of the most acknowledged algorithms show how the former one can efficiently reach a more uniform and complete coverage under a wide range of working scenarios

Voronoi-based deployment of mobile sensors in the face of adversaries

“…We adopt the energy cost model commonly used in the literature for mobile sensors [2], [6], [14]. In particular, receiving a message costs 1 energy units (eu), sending a message 1.125eu, traversing one meter costs 300eu and starting/stopping a movement costs as one meter of movement.…”

“…In order to fully exploit the monitoring capabilities of these networks, distributed deployment algorithms have been designed to let mobile sensors selfdeploy over an Area of Interest (AoI). These algorithms can be roughly classified in three major families on the basis of regular patterns [2], [3], virtual force models [4], [5] or computational geometry [6], [7], [8].…”

“…Hence even if two sensors, at a distance at most Rtx/2, move in opposite directions, they will stop at a distance from each other less than Rtx/2 + 2(Rtx/4 − Rs) which is less than Rtx. This means that Q (t-1) (s) ⊆ N (t) (s), so if a sensor in Q (t-1) (s) is not in N (t) (s), s can mark it as untrusted 2. Notice that, the trustworthiness of the sensors belonging to Q (t) (s) \ Q (t-1) (s) will be evaluated at the next round.…”

Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network

“…This approach provides an uniform deployment in a distributed way, but is not suitable to use in scenarios with obstacles. Another distributed algorithm, known as pull & push [21], produce an hexagonal tiling, by attracting the nodes in low density regions and repulsing nodes from high density regions. This algorithm does not require manual tuning of variables related to the particular working scenario, even if it works only in absence of obstacles.…”

MobiBar: Barrier Coverage with Mobile Sensors

“…Sensors consume energy for communications (sending and receiving messages), and movements (starting/stopping movements and traversing the desired distance). The figure shows a cumulative energy consumption metric expressed in energy units calculated on the energy cost model adopted in [4], [18], [5], [19]. In particular, receiving a message costs 1 unit, sending a message costs 1.125 units, 1m movement and starting/stopping a movement cost the equivalent of sending 300 messages.…”